@Article{Wittek:2014:ANS,
  author =       "Peter Wittek",
  title =        "Algorithm 950: {Ncpol2sdpa} — Sparse Semidefinite
                  Programming Relaxations for Polynomial Optimization
                  Problems of Noncommuting Variables",
  journal =      "{ACM} Transactions on Mathematical Software",
  volume =       "41",
  number =       "3",
  accepted =     "09 October 2014",
  upcoming =     "true", 
  abstract =     "
                  A hierarchy of semidefinite programming (SDP) relaxations
                  approximates the global optimum of polynomial optimization
                  problems of noncommuting variables. Generating the
                  relaxation, however, is a computationally demanding task,
                  and only problems of commuting variables have efficient
                  generators. We develop an implementation for problems of
                  noncommuting variables that creates the relaxation to be
                  solved by SDPA –-- a high-performance solver that runs
                  in a distributed environment.  We further exploit the
                  inherent sparsity of optimization problems in quantum
                  physics to reduce the complexity of the resulting
                  relaxations. Constrained problems with a relaxation of
                  order two may contain up to a hundred variables. The
                  implementation is available in Python. The tool helps
                  solve problems such as finding the ground state energy or
                  testing quantum correlations.",
}